Data analytics refers to the process of reviewing large amounts of raw or unorganized data to formulate conclusions from the data. It is used frequently in business to generate plans of action or to identify patterns and trends in business and to help companies better understand customer behavior. It is also used by economists and academic professionals in many disciplines to help formulate, support or disprove theories.

In many situations, large amounts of data are collected to be studied. For example, economists may receive thousands of survey responses or may look through endless amounts of government and census data on huge segments of the population. Other academics may also receive numerous large bodies of unorganized information; for example, a scientist studying a potential cure for cancer may receive the test results of hundreds, or even thousands or millions, of patients. In business, data may also be collected in the form of sales data, customer receipts, transactions or other types of information.

All of this data provides information and likely contains patterns and trends that can help shape and govern behavior. To use the information, however, the data must be organized, analyzed and understood. Data analytics refers to the process of organizing and analyzing all of that data.

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Just as there are many different types and sources for data, there are many different methods of analytics. Some data must be organized manually and hand coded. Other large databases of information can be sifted through using specialized computer programs which make the process of data analytics streamlined and simple.

The process and procedure of data analytics depends not only on how the data is organized but also on what a person is looking for. For example, an economist may look through data to find patterns of buying or spending that explain behavior. A business may look through data to identify weaknesses in the customer supply chain or problems with a given employee.

Each business generally develops its own methods of data analytics that permit it to solve the problems the given company has. A health insurance company, for example, may have a database of millions of claims paid. Employees in charge of data analysis would be responsible for generating and running algorithms to detect potential abnormalities. The computer program and algorithms could thus be run to identify areas in which false claims may have been paid out that should be investigated.

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